High-strength screw production line cooperative system based on distributed control

Abstract

The invention specifically relates to the technical field of distributed control, and discloses a high-strength screw production line cooperation system based on distributed control, which comprises a distributed acquisition module, a quality analysis and evaluation module, a production parameter control module, a dynamic task scheduling module, an edge local decision module and a global optimization feedback module, constructing a high-spiral distributed sensing data set through a distributed acquisition module; the high spiral production quality grade is evaluated through the quality analysis and evaluation module; a production equipment adjusting instruction is generated through a production parameter control module; generating a task allocation instruction through a dynamic task scheduling module; a terminal control instruction is generated through an edge local decision module, and production equipment execution data is obtained; a global optimization strategy is formulated through a global optimization feedback module; through the cooperative effect of the distributed acquisition module and the edge local decision module, the data transmission amount is reduced by constructing the cloud edge cooperative architecture, and the system architecture flexibility and the data processing efficiency are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of distributed control, more particularly, the present application relates to a high-strength screw production line coordination system based on distributed control. BACKGROUND

[0002] As an important component in the field of mechanical connection, the quality and performance of high-strength screws directly affect the safety and reliability of the overall equipment. With the increasing market competition and quality requirements, the precision, strength and consistency of high-strength screws are also increasing. With the development of industrial internet of things and edge computing, high-speed and stable communication between production equipment is realized, which provides the possibility for the realization of distributed control architecture and the coordinated operation of production lines.

[0003] The existing screw production line control system includes real-time monitoring and data acquisition module, task allocation and production scheduling module, quality control module, data analysis optimization module and human-computer interaction management module, which realizes full-process automation and significantly improves production efficiency and quality stability.

[0004] However, there are still some shortcomings in actual use. First, the architecture flexibility is insufficient. The existing screw production line control system mostly adopts centralized control architecture, with data and control logic concentrated in a central node. System expansion is difficult, and professional personnel need to reprogram when adding new equipment or adjusting the process, resulting in poor system flexibility. Second, the data processing efficiency is limited. The data of the existing screw production line control system needs to be transmitted to the central node for centralized processing and decision-making. The long transmission path leads to long data delay, which may cause data congestion due to bus bandwidth limitation. The existing system relies on the computing power of the central controller, which is prone to memory resource overload, resulting in limited data processing efficiency. Third, the system reliability is low. The centralized control architecture of the existing screw production line control system has the problem of single point failure. If the central control node fails to work, it is easy to cause the whole production line to stop working. The centralized architecture of the existing system is difficult to quickly locate the fault source, and the complexity of fault troubleshooting is high. SUMMARY

[0005] Therefore, the embodiments of the present application provide a high-strength screw production line coordination system based on distributed control. A global optimization feedback module is used to develop a global optimization strategy. Through the synergistic effect of the distributed acquisition module and the edge local decision module, the control authority is decentralized. By building a cloud-edge collaboration architecture, the data transmission amount is reduced, the system architecture flexibility and data processing efficiency are improved, and the problems of insufficient architecture flexibility, limited data processing efficiency and low system reliability in the background technology are effectively solved.

[0006] To achieve the above object, the present application provides the following technical solutions: a high-strength screw production line collaboration system based on distributed control, comprising production equipment terminals, intelligent detection terminals, edge computing terminals, cloud platforms, and human-computer interaction terminals, and further comprising a distributed acquisition module, a quality analysis and evaluation module, a production parameter control module, a dynamic task scheduling module, an edge local decision module, and a global optimization feedback module: The distributed acquisition module acquires high-screw distributed sensing data, constructs a high-screw distributed sensing data set, and passes it to the quality analysis and evaluation module. The quality analysis and evaluation module includes a high-screw comprehensive quality evaluation model, calculates a high-screw comprehensive quality evaluation index based on the high-screw distributed sensing data set, evaluates the high-screw production quality grade, and passes it to the production parameter control module. The production parameter control module formulates production equipment adjustment strategies based on the high-screw production quality grade, generates production equipment adjustment instructions, and passes them to the dynamic task scheduling module. The dynamic task scheduling module acquires real-time production equipment operating parameters, formulates dynamic task scheduling strategies in combination with the production equipment adjustment instructions, generates task allocation instructions, and passes them to the edge local decision module. The edge local decision module generates terminal control instructions based on the task allocation instructions, issues them to the production equipment terminals to execute the terminal control instructions, acquires real-time production equipment execution data, and passes it to the global optimization feedback module. The global optimization feedback module formulates global optimization strategies based on the production equipment execution data in combination with historical data stored in the cloud platform, issues them to the edge computing terminals, and executes the global optimization strategies.

[0007] The technical effects and advantages of the present application are as follows: 1. The present application achieves independent data acquisition and basic analysis of each production equipment terminal through the collaborative action of the distributed acquisition module and the edge local decision module, realizes the decentralization of control authority, and significantly improves the flexibility of the system architecture by only needing to connect the new equipment to the edge computing terminal and complete simple parameter configuration. 2. The edge local decision module carried by the edge computing terminal performs real-time local processing on the high-screw distributed sensing data, only uploads key data to the cloud platform, reduces data transmission volume, and formulates global optimization strategies through the global optimization feedback module deployed on the cloud platform, issues them to the edge computing terminal to execute the strategies, avoids the problem of single node algorithm overload, and improves data processing efficiency. 3. This invention monitors and evaluates the overall quality of the high-speed spiral in real time through a distributed acquisition module and a quality analysis and evaluation module. When an anomaly occurs at a certain edge node, the anomaly source is quickly located by evaluating the production quality level of the high-speed spiral and combining the secondary parameters in the process of calculating the overall quality evaluation index of the high-speed spiral, thereby enhancing the reliability of the system. Attached Figure Description

[0008] Figure 1 This is a schematic diagram of the overall structure of the present invention.

[0009] Figure 2 This is a schematic diagram of the terminal connection according to the present invention.

[0010] Figure 3 This is a schematic diagram illustrating the steps for formulating the dynamic task scheduling strategy of the present invention. Detailed Implementation

[0011] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0012] As attached Figure 1 The system shown is a collaborative system for a high-strength screw production line based on distributed control. It includes a production equipment terminal, an intelligent detection terminal, an edge computing terminal, a cloud platform, and a human-machine interaction terminal. It also includes a distributed acquisition module, a quality analysis and evaluation module, a production parameter control module, a dynamic task scheduling module, an edge local decision-making module, and a global optimization feedback module.

[0013] In a more specific application of the present invention, the production equipment terminal is used to perform the core process of high-strength screw production, receive control commands to adjust operating parameters, and provide real-time feedback on equipment status. The production equipment terminal consists of a processing equipment body, an embedded controller, a drive unit, and status sensors. The production equipment terminal is connected to the edge computing terminal via an industrial Ethernet, with a transmission latency of less than 10ms.

[0014] The intelligent inspection terminal is used to perform quality inspection on screws during the production process, generate quality data and transmit it to the edge computing terminal. The intelligent inspection terminal includes inspection equipment, image sensor / force sensor, local processing unit and data cache unit. The intelligent inspection terminal is directly connected to the edge computing terminal through industrial Ethernet and supports triggered transmission.

[0015] The edge computing terminal is a local control core, used for receiving data of the production equipment terminal and the intelligent detection terminal, performing real-time analysis and decision-making, and coordinating the collaborative work of each terminal, and the edge computing terminal is composed of an edge server, a local database, a communication interface module and a security encryption unit, and the edge computing terminal is connected to the cloud platform through a 5G network.

[0016] The cloud platform is used for storing historical data of the whole production line, performing global optimization analysis, generating long-term production strategies and pushing them to the edge computing terminal, and the cloud platform includes a cloud server cluster, a distributed database, an algorithm engine and a Web management platform.

[0017] The man-machine interaction terminal is used to provide a visual operation interface for an operator to monitor and adjust the production state and query historical data reports, and the man-machine interaction terminal is composed of an industrial touch screen, a mobile operation panel, a data display unit and an instruction input unit, and the man-machine interaction terminal is connected to the edge computing terminal through WIFI, adopts an MQTT protocol to receive real-time data, and sends operation instructions with a response delay ≤500ms.

[0018] The connection mode of the above production equipment terminal, intelligent detection terminal, edge computing terminal, cloud platform and man-machine interaction terminal is shown in Figure 2 .

[0019] The specific embodiments of the present application include the following contents: The distributed acquisition module acquires high-spiral distributed sensing data, constructs a high-spiral distributed sensing data set, and transmits it to the quality analysis and evaluation module. Further, the high-spiral distributed sensing data includes production equipment running parameters, production equipment state parameters and product quality detection parameters, the production equipment running parameters and the production equipment state parameters of the production equipment terminal are acquired through the distributed acquisition module, and the product quality detection parameters of the intelligent detection terminal are acquired.

[0020] In this embodiment, it is specifically pointed out that the production equipment running parameters include cold heading machine main cylinder pressure P, heat treatment furnace quenching zone temperature T, thread rolling machine rolling speed V r , wire feeding speed V s and cooling medium flow rate V c ; the production equipment state parameters include device real-time load rate L, key component temperature T c and main motor real-time current I; and the product quality detection parameters include screw tightening torque M, diameter deviation D, tensile strength σ and hardness value H.

[0021] The distributed acquisition module is deployed in the edge computing terminal.

[0022] It needs to be further explained that the main cylinder pressure of the cold header directly determines the forming density and dimensional accuracy of the screw blank, the quenching zone temperature of the heat treatment furnace affects the metallographic structure transformation of the screw material, the rolling speed of the thread rolling machine is too fast, which easily leads to the decrease of thread accuracy, and too slow affects the production efficiency, the unstable wire feeding speed easily leads to the length deviation of the screw exceeding the standard, and the flow rate of the cooling medium affects the cooling rate of the screw after heat treatment; the real-time load rate of the equipment reflects whether the equipment running load is within the safety threshold, the temperature of the key components including the motor stator temperature and the bearing seat temperature reflects the health status of the equipment components, and the real-time current of the main motor indirectly reflects the change of the equipment running resistance; the screw tightening torque directly determines the reliability of the screw connection, the diameter deviation affects the fitting accuracy of the screw and the nut, the tensile strength measures the ability of the screw to withstand axial tension, and the hardness value reflects the wear resistance and anti-deformation ability of the screw material.

[0023] It needs to be specifically explained that the production equipment running parameters are directly obtained from the core equipment in the production equipment terminal, the parameters are collected through the special sensors installed on the equipment, and are uploaded to the distributed collection module in the edge computing terminal through the interface of the production equipment terminal; the production equipment state parameters are obtained through the state monitoring function of the production equipment terminal and the supplementary collection of external sensors; the product quality detection parameters are detected through the professional detection equipment in the intelligent detection terminal on the semi-finished product / finished product in the screw production process, and are uploaded to the edge computing terminal.

[0024] The high screw distributed sensing data set is the high screw distributed sensing data after standardization processing, and the sampling frequency is uniform. The edge computing terminal and the cloud platform distributed storage architecture are adopted. The edge computing terminal stores recent high-frequency real-time data, meets the local fast calling demand, the cloud platform stores full historical data, adopts a distributed database, and supports multi-dimensional fast retrieval.

[0025] The quality analysis and evaluation module includes a high screw comprehensive quality evaluation model, calculates a high screw comprehensive quality evaluation index based on the high screw distributed sensing data set, evaluates the high screw production quality grade, and transmits to the production parameter control module. The quality analysis and evaluation module is deployed in the edge computing terminal.

[0026] Further, the calculation steps of the high screw comprehensive quality evaluation index are as follows: S1.1: Set a time window, obtain the high screw distributed sensing data set in the time window, import the high screw comprehensive quality evaluation model, calculate the main cylinder pressure fluctuation coefficient C p and the motor current variation coefficient C i through the production equipment running parameters, calculate the rolling-feeding speed matching degree K v and the quenching-cooling coordination coefficient K t, the size deviation qualified rate R is calculated through the product quality detection parameter d and the mechanical property comprehensive index K m ; In this embodiment, it needs to be specifically pointed out that the master cylinder pressure fluctuation coefficient C p The ratio of the standard deviation and the average value of the main cylinder pressure P in the target time window needs to be obtained, and the formula is: , is calculated, wherein n is the number of acquisitions in the target time window, P i is the i-th pressure value, and P1 is the average pressure value in the time window; the motor current variation coefficient C i needs to be calculated by obtaining the ratio of the standard deviation and the average value of the main motor current I in the target time window; the rolling-feeding speed matching degree K v needs to be obtained based on the thread pitch d0 of the thread rolling machine, the rolling speed V r and the wire feeding speed V s in the target window, and the formula is: , is calculated, wherein d0 is a fixed process parameter with a unit of mm; the quenching-cooling coordination coefficient K t needs to be obtained based on the quenching temperature threshold T b and the cooling flow rate threshold V cb , the quenching zone temperature T and the cooling medium flow rate V c of the heat treatment furnace in the target time window, and the formula is: , is calculated, wherein T b may be 850℃, and V cb may be 5L / min; the size deviation qualified rate R d needs to be obtained by counting the proportion of the product quantity with an absolute value of the diameter deviation D ≤ the allowable deviation D b in the target time window; the mechanical property comprehensive index K m needs to obtain the screw tightening torque M, the tensile strength σ and the hardness value H in the target time window, and the formula is: , is calculated, wherein M b , σ b and H b respectively represent the standard value of the screw tightening torque, the tensile strength and the hardness value, which can be 8N·m, 800MPa and 250HV respectively.

[0027] S1.2: the main cylinder pressure fluctuation coefficient C p, motor current variation coefficient C i , rolling-feeding speed matching degree K v , quenching-cooling synergy coefficient K t , size deviation qualified rate R d , and mechanical property comprehensive index K m , and the normalized secondary parameters are represented as C P , C I , K V , K T , R D , and K M , respectively. In this embodiment, it needs to be specifically explained that the master cylinder pressure fluctuation coefficient and the motor current variation coefficient in the positive coefficient are normalized. Taking the master cylinder pressure fluctuation coefficient as an example, the normalization processing is performed through the formula: , The logarithmic function is used for smoothing, and the impact of extreme fluctuations on the overall index is weakened. The rolling-feeding speed matching degree, the quenching-cooling synergy coefficient, the size deviation qualified rate, and the mechanical property comprehensive index are normalized. Taking the mechanical property comprehensive index as an example, the normalization processing is performed through the formula: , The rolling-feeding speed matching degree should be processed by min(1, K v ) before being substituted into the formula for normalization processing, so as to avoid the condition of excessive positive. The exponential function is used for strengthening. When the parameter is close to 1, the index quickly approaches the upper limit. When the parameter is lower than 0.8, the index significantly decreases, and the influence of reaching the standard is highlighted.

[0028] S1.3: Based on the normalized master cylinder pressure fluctuation coefficient, the motor current variation coefficient, the rolling-feeding speed matching degree, the quenching-cooling synergy coefficient, the size deviation qualified rate, and the mechanical property comprehensive index, the high helix comprehensive quality evaluation index Q is calculated through the formula: ,

[0029] In this embodiment, it needs to be specifically explained that the value range of the high helix comprehensive quality evaluation index is 0-1. The closer Q is to 1, the higher the comprehensive quality of the high helix is. If there is a situation that a certain parameter is seriously out of standard, the value of Q is 0. The master cylinder pressure fluctuation coefficient reflects the pressure stability of the equipment, the motor current variation coefficient reflects the motor load stability, the rolling-feeding speed matching degree reflects the rolling-feeding synergy, the quenching-cooling synergy coefficient reflects the heat treatment-cooling synergy, the size deviation qualified rate represents the size processing consistency, and the mechanical property comprehensive index reflects the mechanical comprehensive level. ​

[0030] Further, the evaluation of the high coil production quality level needs to construct high coil production quality level division standards based on the evaluation system principles, including the first quality level, the second quality level, the third quality level, the fourth quality level and the fifth quality level, and evaluate the high coil production quality level based on the high coil comprehensive quality evaluation index and the high coil production quality level division standards.

[0031] In this embodiment, it needs to be further explained that the evaluation system principles include the threshold anchoring principle, the dynamic adaptation principle and the short board correlation principle, wherein the threshold anchoring principle means that the level threshold needs to match the actual production scene, the first quality level corresponds to customer zero complaint and batch qualified rate ≥ 99%; the dynamic adaptation principle means that the threshold is allowed to be fine-tuned according to the product type, not more than 0.03; the short board correlation principle is to associate the level evaluation with the secondary parameters, so as to avoid the situation that a single index covers up the key problems.

[0032] It needs to be specifically explained that the judgment steps of the high coil production quality level are as follows: S2.1: when 0.9≤Q≤1, it is in the first quality level, all the standardized values of the secondary parameters are ≥0.85, which means that the production whole process is optimal, and the customer satisfaction is ≥99.5%; S2.2: when 0.8≤Q<0.9, it is in the second quality level, only one item of the standardized values of the secondary parameters is ≥0.75 and <0.85, which means that the production process is basically stable and does not affect the customer use; S2.3: when 0.7≤Q<0.8, it is in the third quality level, allowing 2 items and less of the standardized values of the secondary parameters to be ≥0.65 and <0.75, which means that there is obvious optimization space in the production; S2.4: when 0.6≤Q<0.7, it is in the fourth quality level, there is mechanical performance comprehensive index <0.8 and size deviation qualified rate <0.8, which means that there is abnormality in the key link of the production process, and the process needs to be urgently optimized; S2.5: when Q<0.6, it is in the fifth quality level, any one item of the standardized values of the secondary parameters is 0, which means that the production process is out of control, and all products are prohibited to deliver.

[0033] Production parameter control module: based on the high coil production quality level, the production equipment adjustment strategy is formulated, the production equipment adjustment instruction is generated, and is delivered to the dynamic task scheduling module; Further, the formulation of the production equipment adjustment strategy needs to formulate the production equipment adjustment strategy of the quality level based on the high coil production quality level and the corresponding secondary parameter short board item, which means that the secondary parameter C P , C I , KV , K T , R D , and K M a parameter less than 0.8.

[0034] In this embodiment, it needs to be specifically pointed out that for the equipment in the first quality level, the current stable state of the equipment needs to be maintained to avoid excessive adjustment to destroy the existing balance, only the fine adjustment of the small fluctuation parameter is made, the single parameter adjustment amplitude is less than or equal to 2%, and 2 or more equipment parameters cannot be adjusted at the same time; for the equipment in the second quality level, 1 non-key short board needs to be optimized, the single parameter adjustment amplitude is 2%-5%, and the production equipment operation parameter is adjusted preferentially; for the equipment in the third quality level, 2 or less short boards need to be solved to ensure that the product qualified rate is improved to more than 98%, the key parameter adjustment amplitude is 5%-15%, and after adjustment, small batch trial production verification is needed; for the equipment in the fourth quality level, the key short board problem needs to be solved, the main line production needs to be suspended, small batch debugging needs to be started, the key parameter adjustment amplitude is 5%-15%, and after adjustment, Q needs to be recalculated; for the equipment in the fifth quality level, production is prohibited, the equipment failure or process error is fully investigated, and after adjustment, device calibration, trial production and quality re-inspection are needed to ensure that the risk is completely eliminated.

[0035] It needs to be specifically pointed out that the production equipment adjustment instruction adopts JSON format, including instruction ID, target equipment, adjustment parameter, quality level, execution priority and verification requirement; The production parameter control module is deployed on the edge computing terminal, the production equipment adjustment instruction is transmitted to the edge computing terminal by the production parameter control module, and then transmitted to the dynamic task scheduling module by the edge computing terminal.

[0036] The dynamic task scheduling module: obtains real-time production equipment operation parameters, formulates a dynamic task scheduling strategy in combination with the production equipment adjustment instruction, generates a task allocation instruction, and transmits it to the edge local decision-making module; The dynamic task scheduling module is deployed on the edge computing terminal and the cloud platform, the edge computing terminal processes short-term task scheduling, and the cloud platform processes long-term task planning.

[0037] Further, as shown in Figure 3 , the steps of formulating the dynamic task scheduling strategy are as follows: S3.1: generate a device availability matrix based on real-time production equipment operation parameters, obtain order task demand in the cloud platform, construct a task demand pool, and obtain the equipment to be adjusted and the capacity of the equipment after adjustment based on the production equipment adjustment instruction; S3.2: Based on the equipment availability matrix, the task demand pool, and the adjusted equipment capacity, preliminary equipment screening is performed to exclude equipment that does not meet the adjusted equipment capacity or is in the adjustment / failure state, and an adaptive equipment list is obtained. S3.3: Based on the adaptive equipment list and the task demand pool, a load balancing and priority sorting algorithm is used to prioritize emergency orders and reserve equipment adjustment time, and a dynamic task scheduling strategy is generated.

[0038] In this embodiment, it needs to be specifically explained that the real-time state of the production equipment in the equipment availability matrix is normal / adjustment / failure; the order task demand in the task demand pool includes order number, screw type, output, delivery priority, and quality grade requirement, wherein the delivery priority is divided into emergency and regular, and the quality grade requirement is divided into excellent and good; the adjusted equipment capacity refers to the ability of the equipment to be adjusted to process subsequent production steps after adjustment according to the equipment adjustment instruction, for example, the adjusted heat treatment furnace can meet the quenching demand of 12.9 grade screw.

[0039] It needs to be specifically explained that the task allocation instruction adopts JSON format, including instruction ID, target equipment, task information, equipment coordination information, associated adjustment instruction, and progress feedback requirement, wherein the task information includes order number, screw type, output, quality requirement, and completion time limit.

[0040] Edge local decision module: based on the task allocation instruction, terminal control instruction is generated, and terminal control instruction is issued to the production equipment terminal to execute the terminal control instruction, and real-time production equipment execution data is obtained and transmitted to the global optimization feedback module; The edge local decision module is deployed in the edge computing terminal.

[0041] Further, the terminal control instruction needs to be combined with the parameter-action mapping library of the production equipment terminal, and is generated based on the task allocation instruction. The terminal control instruction includes action timing, parameter threshold, and abnormal trigger condition.

[0042] In this embodiment, it needs to be specifically explained that the parameter-action mapping library should preset the corresponding relationship between each device parameter and physical action, for example, the cold heading machine pressure 50MPa corresponds to the hydraulic valve opening degree 60%, which is used to convert the process parameters in the task allocation instruction into control parameters that can be recognized by the equipment.

[0043] Further, the production equipment execution data includes dynamic state data, process parameter data, quality correlation data, and equipment health data, which are collected by the production equipment terminal according to the frequency of 1 second / time.

[0044] In this embodiment, it needs to be specifically pointed out that the dynamic state data includes the current execution step, the completed yield, the remaining yield and the material arrival state; the process parameter data includes the real-time pressure of the main cylinder, the real-time speed of the feeding, the punch stroke and the forming pressure maintaining time; the quality correlation data includes the pressure fluctuation amplitude, the feeding speed deviation and the forming time fluctuation of each batch; and the equipment health data includes the real-time current of the main machine, the hydraulic system oil temperature, the mold temperature and the equipment debt rate.

[0045] The global optimization feedback module: based on the production equipment execution data, combined with the historical data stored in the cloud platform, a global optimization strategy is formulated, which is issued to the edge computing terminal to execute the global optimization strategy.

[0046] The global optimization feedback module is deployed in the cloud platform.

[0047] Further, the global optimization strategy needs to form a real-time-historical data set based on the production equipment execution data and the historical data, use correlation analysis and time series pattern mining to locate the system reasons affecting the high screw comprehensive quality evaluation index, identify the efficiency loss nodes, and locate the optimizable cost items, combined with the industry process standards and historical optimal parameters.

[0048] In this embodiment, it needs to be specifically pointed out that the edge computing terminal uploads 1 batch of production equipment execution data every 1 minute, covering all devices such as cold header, heat treatment furnace and thread rolling machine, and the historical data refers to the production data, quality grade record and equipment maintenance log of the same type of screw line in the past 3 months; the identification of the efficiency loss nodes needs to analyze the device utilization rate, process balance rate and batch change time; and the positioning of the optimizable cost items needs to count the energy consumption and material loss; the industry process standards and historical optimal parameters are all from the database of the cloud platform.

[0049] It needs to be specifically pointed out that after the global optimization strategy is generated, the global optimization strategy needs to be converted into a standardized parameter adjustment scheme, which is issued to the edge computing terminal, the production equipment execution data is continuously collected, the key indicators before and after the strategy execution are compared, such as the high screw comprehensive quality evaluation index, the optimization effect is verified, and if the optimization does not reach the expectation, the problem diagnosis stage needs to be returned to iterate the strategy.

[0050] Secondly: in the drawings of the disclosed embodiments, only the structures related to the disclosed embodiments are involved, other structures can be referred to the usual design, and in the case of no conflict, the same embodiment and different embodiments of the present application can be combined with each other; Finally: the above only describes the preferred embodiments of the present application, and is not used to limit the present application, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A high-strength screw production line collaboration system based on distributed control, characterized by, The system comprises production equipment terminals, intelligent detection terminals, edge computing terminals, cloud platforms, and human-computer interaction terminals, and further comprises a distributed acquisition module, a quality analysis and evaluation module, a production parameter control module, a dynamic task scheduling module, an edge local decision module, and a global optimization feedback module. The distributed acquisition module acquires high-spiral distributed sensing data, constructs a high-spiral distributed sensing data set, and transmits the data set to the quality analysis and evaluation module. The quality analysis and evaluation module comprises a high-spiral comprehensive quality evaluation model, calculates a high-spiral comprehensive quality evaluation index based on the high-spiral distributed sensing data set, evaluates the high-spiral production quality grade, and transmits the evaluation result to the production parameter control module. The production parameter control module formulates a production equipment adjustment strategy based on the high-spiral production quality grade, generates a production equipment adjustment instruction, and transmits the instruction to the dynamic task scheduling module. The dynamic task scheduling module acquires real-time production equipment operation parameters, formulates a dynamic task scheduling strategy in combination with the production equipment adjustment instruction, generates a task allocation instruction, and transmits the instruction to the edge local decision module. The edge local decision module generates a terminal control instruction based on the task allocation instruction, issues the instruction to the production equipment terminal to execute the terminal control instruction, acquires real-time production equipment execution data, and transmits the data to the global optimization feedback module. The global optimization feedback module formulates a global optimization strategy based on the production equipment execution data in combination with historical data stored in the cloud platform, issues the strategy to the edge computing terminal, and executes the global optimization strategy.

2. The high-strength screw production line collaboration system based on distributed control according to claim 1, characterized in that: The high-spiral distributed sensing data comprises production equipment operation parameters, production equipment state parameters, and product quality detection parameters.

3. The high-strength screw production line collaboration system based on distributed control according to claim 1, characterized in that: The calculation steps of the high-spiral comprehensive quality evaluation index are as follows: S1.1: Set a time window, obtain the high helix distributed sensing data set in the time window, import it into the high helix comprehensive quality evaluation model, calculate the master cylinder pressure fluctuation coefficient C through the production equipment operation parameters p And motor current variation coefficient C i , the rolling-feeding speed matching degree K v And quenching-cooling coordination coefficient K t , the size deviation qualified rate R d And mechanical property comprehensive index K m ; S1.2: the master cylinder pressure fluctuation coefficient C in the secondary parameters p , the motor current variation coefficient C i , the rolling-feeding speed matching degree K v , the quenching-cooling coordination coefficient K t , the size deviation qualified rate R d , and the mechanical property comprehensive index K m are standardized to obtain the standardized secondary parameters, which are respectively represented as C P , C I , K V , K T , R D , and K M ; S1.3: Based on the normalized master cylinder pressure fluctuation coefficient, motor current variation coefficient, rolling-feeding speed matching degree, quenching-cooling coordination coefficient, size deviation qualified rate and mechanical property comprehensive index, the high helix comprehensive quality evaluation index Q is calculated by formula: ​ 4. The high-strength screw production line collaboration system based on distributed control according to claim 1, characterized in that: The evaluation of the high-spiral production quality grade needs to construct high-spiral production quality grade division standards based on evaluation system principles, including a first quality grade, a second quality grade, a third quality grade, a fourth quality grade, and a fifth quality grade.

5. The high-strength screw production line collaboration system based on distributed control according to claim 1, characterized in that: The production equipment adjustment strategy needs to be formulated based on the high helix production quality grade and the corresponding secondary parameter short board item. The secondary parameter short board item refers to the parameter less than 0.8 in the calculation process of the high helix comprehensive quality evaluation index. P I V T D M ​​​​​​ 6. The high-strength screw production line collaboration system based on distributed control according to claim 1, characterized in that: The formulation steps of the dynamic task scheduling strategy are as follows: S3.1: Generate a device availability matrix based on real-time production equipment operation parameters, acquire order task demands in the cloud platform, construct a task demand pool, acquire devices to be adjusted and adjusted device capabilities based on the production equipment adjustment instruction; S3.2: Perform preliminary device screening based on the device availability matrix, the task demand pool, and the adjusted device capabilities, exclude devices that do not meet the adjusted device capabilities or are in the adjustment / fault state, and obtain an adaptive device list; S3.3: Based on the adaptive device list and the task demand pool, use load balancing and priority sorting algorithms to prioritize emergency orders and reserve device adjustment time, and generate a dynamic task scheduling strategy.

7. The high-strength screw production line collaboration system based on distributed control according to claim 1, characterized in that: The terminal control instruction needs to be combined with the parameter-action mapping library of the production equipment terminal, and is generated based on the task allocation instruction. The terminal control instruction includes action timing, parameter threshold, and abnormal trigger condition.

8. The high-strength screw production line collaboration system based on distributed control according to claim 1, characterized in that: The production equipment execution data includes dynamic state data, process parameter data, quality correlation data, and equipment health data. The production equipment execution data is collected by the production equipment terminal according to a frequency of 1 second / time.

9. The high-strength screw production line collaboration system based on distributed control according to claim 1, characterized in that: The global optimization strategy needs to form a real-time-historical data set based on production equipment execution data and historical data, use correlation analysis and time series pattern mining to locate the system reasons affecting the high spiral comprehensive quality evaluation index, identify efficiency loss nodes, and locate the optimizable cost items, combined with industry process standards and historical optimal parameters.

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